5 Steps for Implementing Artificial Intelligence In Your Organization
By Jason Grunberg | August 5, 2019
In the past year, the number of enterprises implementing artificial intelligence has tripled, according to a Gartner survey of more than 3,000 CIOs from around the world. And yet, they view skill shortage as their biggest challenge. That tells us that while AI has never been hotter, it’s also never been more misunderstood.
To help marketers make sense of this transformative technology, Sailthru sponsored Beyond the Hype: How Marketers Can Capitalize on AI, a new whitepaper from Harvard Business Review Analytic Services. One key takeaway is that while AI and machine learning are often misunderstood, they are maturing beyond first-generation chatbots and product recommendations.
For marketers looking to get there, we’ve outlined a five-step modernization plan.
1. Identify business needs and specific requirements of the marketing organization before considering the role of a technology.
Before you adopt artificial intelligence, ask yourself why. Define the use cases and then figure out how AI will help you achieve them, rather than the other way around. As Andrew Perell, director of email strategy and operations at Condé Nast, says, “There’s a temptation with any new and exciting technology to put the cart before the horse — to look for a technology solution before you realize your problems and goals.”
2. Create a strategy for integrating artificial intelligence initiatives within the current marketing operation
That means starting by identifying the changes to data management and governance that will be necessary for AI and machine learning use cases. Companies often struggle with aggregating data, which remains segmented in silos. This results in AI and machine learning analyses that aren’t fully informed. Is your data clean and up-to-date? Aggregated data can make you money; redundant and conflicting records will just make a mess.
3. Develop criteria for evaluating artificial intelligence and machine learning applications, services and vendors.
We recommend looking for vendors with AI and machine learning expertise, of course, as well as open-source application programming interfaces (APIs). These act as ready-made links between the new analytics capabilities, enabling smooth transitions between different communications channels. If you don’t have API capabilities internally, fear not. Many vendors are cloud-based, eliminating the need for dedicated internal IT resources.
4. Manage organizational change.
Historically, marketers launched campaigns by deciding what to distribute. It’s easy to default to what’s worked in the past. However, the strongest campaigns break free of convention. AI enables more of a conversation, which empowers consumers to decide how to engage with brands. Learn from those conversations and use that data to fuel future marketing. AI gives you the tools to deploy the personalized communications — dynamic content, for instance — consumers ultimately crave.
5. Cultivate closer collaboration among marketing, data science, and line-of-business organizations.
Like data, people shouldn’t be siloed. Marketers should communicate with the technology department, which will probably have to develop a new IT infrastructure. The finance department would help monitor the spend there. A cross-functional team with a wide array of expertise performs better and alignment means they’re all marching toward the same objective.
Need more inspiration? Click here to download Beyond the Hype: How Marketers Can Capitalize on AI to learn how brands like Condé Nast and JustFab are looking past first-generation chatbots and product recommendations.
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